People: Arindam Banerjee

Arindam Banerjee

Department of Computer Science and Engineering
4-192, EE/CSci Building
University of Minnesota
http://www-users.cs.umn.edu/~banerjee/
Ph: (612) 625-0041

Arindam Banerjee is an assistant professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He received his Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin, in 2005, M. Tech. in Electrical Engineering from the IIT, Kanpur, India, in 1999, and B. Engg. in Electronics and Tele-communication Engineering from Jadavpur University, India, in 1997.

Banerjee’s research interests are in Data Mining and Machine Learning, primarily in computational learning and predictive modeling with little or no supervision. He has worked on the analysis and design of scalable algorithms for unsupervised and semi-supervised clustering. His research interests also include Information Theory, Convex Analysis, Computational Games, and their applications in complex real world learning problems including problems in Text and Web Mining, Bioinformatics and Social Networks. He has published extensively in top data mining conferences and journals. His work on clustering using Bregman divergences and clustering on the hypersphere is currently the state of the art, and is currently being used in several organizations such as Google, NASA, and Oak Ridge National Labs.

Banerjee has won several fellowships including the prestigious IBM PhD fellowship for the academic years 2003–2004 and 2004–2005, and the J. T. Oden Faculty Research Fellowship from the Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, 2006. He has won several awards for his publications, including the Best Algorithms Paper Award at the SIAM International Conference on Data Mining, 2004, and the Best Research Paper Award under University Cooperative Society Research Excellence Awards, University of Texas at Austin, 2005. His dissertation titled “Scalable Clustering Algorithms” was nominated for the Best Dissertation Award at the University of Texas at Austin.